Verified claim · AI-ML · 100% confidence
ResNet (Residual Networks) introduced in paper: Deep Residual Learning for Image Recognition (He et al., 2015).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 4f55f77c4bfb316e
Structured fields
- Subject
- ResNet (Residual Networks)
- Predicate
introduced_in_paper- Object
- Deep Residual Learning for Image Recognition (He et al., 2015)
- Confidence
- 100%
- Tags
- resnet · foundational · vision · he · 2015 · microsoft · cvpr
Sources (2)
[1] preprint · arXiv (He, Zhang, Ren, Sun) · 2015-12-10
Deep Residual Learning for Image Recognition“We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.”
[2] peer reviewed · IEEE / Computer Vision Foundation · 2016-06-30
Deep Residual Learning (CVPR 2016 proceedings)
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Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/4f55f77c4bfb316e.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/4f55f77c4bfb316e.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "ResNet (Residual Networks) introduced in paper: Deep Residual Learning for Image Recognition (He et al., 2015)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/4f55f77c4bfb316e.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "ResNet (Residual Networks) introduced in paper: Deep Residual Learning for Image Recognition (He et al., 2015)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_resnet_residual_networks_fact() -> dict:
"""Fetch the verified SourceScore claim for ResNet (Residual Networks)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/4f55f77c4bfb316e.json")
return r.json()